library(drc)
## Loading required package: MASS
## Warning: package 'MASS' was built under R version 4.1.2
##
## 'drc' has been loaded.
## Please cite R and 'drc' if used for a publication,
## for references type 'citation()' and 'citation('drc')'.
##
## Attaching package: 'drc'
## The following objects are masked from 'package:stats':
##
## gaussian, getInitial
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.1.2
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.1.2
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:MASS':
##
## select
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(MASS)
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, aad$Country == "Brazil")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$Population))
mean_Population<- aad %>%
group_by(Year) %>%
summarise_at(vars(Population), list(name = mean))
mean_Population
## # A tibble: 18 × 2
## Year name
## <int> <dbl>
## 1 2001 43847.
## 2 2002 45426.
## 3 2003 46348.
## 4 2004 49244.
## 5 2005 48259.
## 6 2006 49831.
## 7 2007 53217.
## 8 2008 54053.
## 9 2009 54114.
## 10 2010 52696.
## 11 2011 55494.
## 12 2012 57312.
## 13 2013 59288.
## 14 2014 56854.
## 15 2015 57243.
## 16 2016 61279.
## 17 2017 61114.
## 18 2018 62421.
plot(mean_Population, main = "Population between 2001 and 2019", xlab = "Year", ylab = "Population")
small_df <- aad %>%
dplyr::select("Cutaneous.Leishmaniasis", "Population")
small_df <- small_df %>%
mutate(
Population = cut(Population, breaks = seq(min(Population), max(Population), by = 75000), include.lowest = TRUE)
) %>%
group_by(Population) %>%
summarise(Cutaneous.Leishmaniasis = mean(Cutaneous.Leishmaniasis))
plot(small_df$Population, small_df$Cutaneous.Leishmaniasis)
ggplot(data = small_df, mapping = aes(Population, Cutaneous.Leishmaniasis)) +
geom_point() +
theme(axis.text.x = element_text(angle = 90))
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, aad$Country == "Colombia")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$Population))
mean_Population<- aad %>%
group_by(Year) %>%
summarise_at(vars(Population), list(name = mean))
mean_Population
## # A tibble: 11 × 2
## Year name
## <int> <dbl>
## 1 2007 66785.
## 2 2008 42080.
## 3 2009 44313.
## 4 2010 44068.
## 5 2011 44255.
## 6 2012 46347.
## 7 2013 46646.
## 8 2014 47657.
## 9 2015 45938.
## 10 2016 43532.
## 11 2017 69874.
plot(mean_Population, main = "Population between 2001 and 2019", xlab = "Year", ylab = "Population")
small_df <- aad %>%
dplyr::select("Cutaneous.Leishmaniasis", "Population")
small_df <- small_df %>%
mutate(
Population = cut(Population, breaks = seq(min(Population), max(Population), by = 25000), include.lowest = TRUE)
) %>%
group_by(Population) %>%
summarise(Cutaneous.Leishmaniasis = mean(Cutaneous.Leishmaniasis))
plot(small_df$Population, small_df$Cutaneous.Leishmaniasis)
ggplot(data = small_df, mapping = aes(Population, Cutaneous.Leishmaniasis)) +
geom_point() +
theme(axis.text.x = element_text(angle = 90))
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, aad$Country == "Peru")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$Population))
mean_Population<- aad %>%
group_by(Year) %>%
summarise_at(vars(Population), list(name = mean))
mean_Population
## # A tibble: 10 × 2
## Year name
## <int> <dbl>
## 1 2010 10771.
## 2 2011 10038.
## 3 2012 11572.
## 4 2013 12103.
## 5 2014 13226.
## 6 2015 14424.
## 7 2016 14222.
## 8 2017 17294.
## 9 2018 13767.
## 10 2019 13872.
plot(mean_Population, main = "Population between 2001 and 2019", xlab = "Year", ylab = "Population")
small_df <- aad %>%
dplyr::select("Cutaneous.Leishmaniasis", "Population")
small_df <- small_df %>%
mutate(
Population = cut(Population, breaks = seq(min(Population), max(Population), by = 5000), include.lowest = TRUE)
) %>%
group_by(Population) %>%
summarise(Cutaneous.Leishmaniasis = mean(Cutaneous.Leishmaniasis))
plot(small_df$Population, small_df$Cutaneous.Leishmaniasis)
ggplot(data = small_df, mapping = aes(Population, Cutaneous.Leishmaniasis)) +
geom_point() +
theme(axis.text.x = element_text(angle = 90))
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$LST_Day))
mean_LST_Day<- aad %>%
group_by(Year) %>%
summarise_at(vars(LST_Day), list(name = mean))
mean_LST_Day
## # A tibble: 19 × 2
## Year name
## <int> <dbl>
## 1 2001 29.6
## 2 2002 29.1
## 3 2003 29.3
## 4 2004 28.8
## 5 2005 29.1
## 6 2006 28.8
## 7 2007 29.0
## 8 2008 28.4
## 9 2009 28.3
## 10 2010 28.1
## 11 2011 27.5
## 12 2012 28.2
## 13 2013 28.0
## 14 2014 28.3
## 15 2015 28.8
## 16 2016 28.3
## 17 2017 27.8
## 18 2018 27.7
## 19 2019 23.8
plot(mean_LST_Day, main = "LST_Day between 2001 and 2019", xlab = "Year", ylab = "LST_Day")
small_df <- aad %>%
dplyr::select("Cutaneous.Leishmaniasis", "LST_Day")
small_df <- small_df %>%
mutate(
LST_Day = cut(LST_Day, breaks = seq(min(LST_Day), max(LST_Day), by = 1), include.lowest = TRUE)
) %>%
group_by(LST_Day) %>%
summarise(Cutaneous.Leishmaniasis = mean(Cutaneous.Leishmaniasis))
plot(small_df$LST_Day, small_df$Cutaneous.Leishmaniasis)
ggplot(data = small_df, mapping = aes(LST_Day, Cutaneous.Leishmaniasis)) +
geom_point()
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$LST_Day))
library(dplyr)
library(ggplot2)
mean_LST_Day<- aad %>%
group_by(Year) %>%
summarise_at(vars(LST_Day), list(name = mean))
mean_LST_Day
## # A tibble: 19 × 2
## Year name
## <int> <dbl>
## 1 2001 29.6
## 2 2002 29.1
## 3 2003 29.3
## 4 2004 28.8
## 5 2005 29.1
## 6 2006 28.8
## 7 2007 29.0
## 8 2008 28.4
## 9 2009 28.3
## 10 2010 28.1
## 11 2011 27.5
## 12 2012 28.2
## 13 2013 28.0
## 14 2014 28.3
## 15 2015 28.8
## 16 2016 28.3
## 17 2017 27.8
## 18 2018 27.7
## 19 2019 23.8
plot(mean_LST_Day, main = "Cutaneous Cases between 2001 and 2019", xlab = "Year", ylab = "Cases")
ggplot(data = aad, mapping = aes(LST_Day, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
set.seed(22)
sample <- sample_n(aad, 500)
ggplot(data = sample, mapping = aes(LST_Day, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$Precip))
mean_Precip<- aad %>%
group_by(Year) %>%
summarise_at(vars(Precip), list(name = mean))
mean_Precip
## # A tibble: 19 × 2
## Year name
## <int> <dbl>
## 1 2001 1335.
## 2 2002 1397.
## 3 2003 1362.
## 4 2004 1528.
## 5 2005 1447.
## 6 2006 1461.
## 7 2007 1546.
## 8 2008 1781.
## 9 2009 1826.
## 10 2010 1534.
## 11 2011 1728.
## 12 2012 1463.
## 13 2013 1635.
## 14 2014 1543.
## 15 2015 1428.
## 16 2016 1449.
## 17 2017 1586.
## 18 2018 1451.
## 19 2019 1115.
plot(mean_Precip, main = "Precip between 2001 and 2019", xlab = "Year", ylab = "Precip")
small_df <- aad %>%
dplyr::select("Cutaneous.Leishmaniasis", "Precip")
small_df <- small_df %>%
mutate(
Precip = cut(Precip, breaks = seq(min(Precip), max(Precip), by = 100), include.lowest = TRUE)
) %>%
group_by(Precip) %>%
summarise(Cutaneous.Leishmaniasis = mean(Cutaneous.Leishmaniasis))
plot(small_df$Precip, small_df$Cutaneous.Leishmaniasis)
ggplot(data = small_df, mapping = aes(Precip, Cutaneous.Leishmaniasis)) +
geom_point()
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$Precip))
ggplot(data = aad, mapping = aes(Precip, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
set.seed(22)
sample <- sample_n(aad, 500)
ggplot(data = sample, mapping = aes(Precip, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$AvgRad))
mean_AvgRad<- aad %>%
group_by(Year) %>%
summarise_at(vars(AvgRad), list(name = mean))
mean_AvgRad
## # A tibble: 6 × 2
## Year name
## <int> <dbl>
## 1 2014 0.974
## 2 2015 0.897
## 3 2016 0.829
## 4 2017 1.13
## 5 2018 1.16
## 6 2019 0.329
plot(mean_AvgRad, main = "AvgRad between 2001 and 2019", xlab = "Year", ylab = "AvgRad")
small_df <- aad %>%
dplyr::select("Cutaneous.Leishmaniasis", "AvgRad")
small_df <- small_df %>%
mutate(
AvgRad = cut(AvgRad, breaks = seq(min(AvgRad), max(AvgRad), by = 1), include.lowest = TRUE)
) %>%
group_by(AvgRad) %>%
summarise(Cutaneous.Leishmaniasis = mean(Cutaneous.Leishmaniasis))
plot(small_df$AvgRad, small_df$Cutaneous.Leishmaniasis)
ggplot(data = small_df, mapping = aes(AvgRad, Cutaneous.Leishmaniasis)) +
geom_point()
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$AvgRad))
ggplot(data = aad, mapping = aes(AvgRad, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
set.seed(22)
sample <- sample_n(aad, 500)
ggplot(data = sample, mapping = aes(AvgRad, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$SWOccurrence))
mean_SWOccurrence<- aad %>%
group_by(Year) %>%
summarise_at(vars(SWOccurrence), list(name = mean))
mean_SWOccurrence
## # A tibble: 19 × 2
## Year name
## <int> <dbl>
## 1 2001 34.8
## 2 2002 36.0
## 3 2003 35.6
## 4 2004 36.0
## 5 2005 35.6
## 6 2006 35.6
## 7 2007 35.9
## 8 2008 35.8
## 9 2009 35.8
## 10 2010 35.0
## 11 2011 35.0
## 12 2012 35.4
## 13 2013 35.7
## 14 2014 35.3
## 15 2015 36.1
## 16 2016 35.9
## 17 2017 35.3
## 18 2018 36.2
## 19 2019 37.1
plot(mean_SWOccurrence, main = "SWOccurrence between 2001 and 2019", xlab = "Year", ylab = "SWOccurrence")
small_df <- aad %>%
dplyr::select("Cutaneous.Leishmaniasis", "SWOccurrence")
small_df <- small_df %>%
mutate(
SWOccurrence = cut(SWOccurrence, breaks = seq(min(SWOccurrence), max(SWOccurrence), by = 1), include.lowest = TRUE)
) %>%
group_by(SWOccurrence) %>%
summarise(Cutaneous.Leishmaniasis = mean(Cutaneous.Leishmaniasis))
plot(small_df$SWOccurrence, small_df$Cutaneous.Leishmaniasis)
ggplot(data = small_df, mapping = aes(SWOccurrence, Cutaneous.Leishmaniasis)) +
geom_point()
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$SWOccurrence))
ggplot(data = aad, mapping = aes(SWOccurrence, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
set.seed(22)
sample <- sample_n(aad, 500)
ggplot(data = sample, mapping = aes(SWOccurrence, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
Note: for the years 2018 and 2019 the forest variables are entirely missing so we will remove these years from this part of the analysis
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, aad$Year < 2018)
aad$pland_forest <- ifelse(is.na(aad$pland_forest), 0, aad$pland_forest)
mean_pland_forest<- aad %>%
group_by(Year) %>%
summarise_at(vars(pland_forest), list(name = mean))
mean_pland_forest
## # A tibble: 17 × 2
## Year name
## <int> <dbl>
## 1 2001 15.3
## 2 2002 15.5
## 3 2003 16.1
## 4 2004 17.0
## 5 2005 16.5
## 6 2006 17.0
## 7 2007 16.0
## 8 2008 15.8
## 9 2009 15.1
## 10 2010 17.4
## 11 2011 18.5
## 12 2012 18.5
## 13 2013 19.3
## 14 2014 19.0
## 15 2015 18.7
## 16 2016 18.8
## 17 2017 19.4
plot(mean_pland_forest, main = "pland_forest between 2001 and 2019", xlab = "Year", ylab = "pland_forest")
small_df <- aad %>%
dplyr::select("Cutaneous.Leishmaniasis", "pland_forest")
small_df <- small_df %>%
mutate(
pland_forest = cut(pland_forest, breaks = seq(min(pland_forest), max(pland_forest), by = 1), include.lowest = TRUE)
) %>%
group_by(pland_forest) %>%
summarise(Cutaneous.Leishmaniasis = mean(Cutaneous.Leishmaniasis))
plot(small_df$pland_forest, small_df$Cutaneous.Leishmaniasis)
ggplot(data = small_df, mapping = aes(pland_forest, Cutaneous.Leishmaniasis)) +
geom_point()
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$pland_forest))
ggplot(data = aad, mapping = aes(pland_forest, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
set.seed(22)
sample <- sample_n(aad, 500)
ggplot(data = sample, mapping = aes(pland_forest, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
library(dplyr)
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, aad$Year < 2018)
aad$te_forest <- ifelse(is.na(aad$te_forest), 0, aad$te_forest)
mean_te_forest<- aad %>%
group_by(Year) %>%
summarise_at(vars(te_forest), list(name = mean))
mean_te_forest
## # A tibble: 17 × 2
## Year name
## <int> <dbl>
## 1 2001 2957377.
## 2 2002 2979479.
## 3 2003 3201279.
## 4 2004 3392664.
## 5 2005 3351710.
## 6 2006 3553386.
## 7 2007 3303576.
## 8 2008 3268245.
## 9 2009 3152663.
## 10 2010 2878924.
## 11 2011 2964732.
## 12 2012 3085098.
## 13 2013 3181605.
## 14 2014 3093772.
## 15 2015 3129279.
## 16 2016 3354560.
## 17 2017 3314219.
plot(mean_te_forest, main = "te_forest between 2001 and 2019", xlab = "Year", ylab = "te_forest")
small_df <- aad %>%
dplyr::select("Cutaneous.Leishmaniasis", "te_forest")
small_df <- small_df %>%
mutate(
te_forest = cut(te_forest, breaks = seq(min(te_forest), max(te_forest), by = 2000000), include.lowest = TRUE)
) %>%
group_by(te_forest) %>%
summarise(Cutaneous.Leishmaniasis = mean(Cutaneous.Leishmaniasis))
plot(small_df$te_forest, small_df$Cutaneous.Leishmaniasis)
ggplot(data = small_df, mapping = aes(te_forest, Cutaneous.Leishmaniasis)) +
geom_point() +
theme(axis.text.x = element_text(angle = 90))
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$te_forest))
ggplot(data = aad, mapping = aes(te_forest, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
set.seed(22)
sample <- sample_n(aad, 500)
ggplot(data = sample, mapping = aes(te_forest, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, aad$Year < 2018)
aad$enn_mn_forest <- ifelse(is.na(aad$enn_mn_forest), 0, aad$enn_mn_forest)
mean_enn_mn_forest<- aad %>%
group_by(Year) %>%
summarise_at(vars(enn_mn_forest), list(name = mean))
mean_enn_mn_forest
## # A tibble: 17 × 2
## Year name
## <int> <dbl>
## 1 2001 28.1
## 2 2002 28.9
## 3 2003 29.5
## 4 2004 31.3
## 5 2005 33.2
## 6 2006 35.8
## 7 2007 33.4
## 8 2008 35.4
## 9 2009 31.2
## 10 2010 33.8
## 11 2011 34.1
## 12 2012 36.3
## 13 2013 35.7
## 14 2014 38.4
## 15 2015 35.4
## 16 2016 36.1
## 17 2017 35.1
plot(mean_enn_mn_forest, main = "enn_mn_forest between 2001 and 2019", xlab = "Year", ylab = "enn_mn_forest")
small_df <- aad %>%
dplyr::select("Cutaneous.Leishmaniasis", "enn_mn_forest")
small_df <- small_df %>%
mutate(
enn_mn_forest = cut(enn_mn_forest, breaks = seq(min(enn_mn_forest), max(enn_mn_forest), by = 25), include.lowest = TRUE)
) %>%
group_by(enn_mn_forest) %>%
summarise(Cutaneous.Leishmaniasis = mean(Cutaneous.Leishmaniasis))
plot(small_df$enn_mn_forest, small_df$Cutaneous.Leishmaniasis)
ggplot(data = small_df, mapping = aes(enn_mn_forest, Cutaneous.Leishmaniasis)) +
geom_point() +
theme(axis.text.x = element_text(angle = 90))
aad <- read.csv("../models/data/aad.csv")
aad <- subset(aad, !is.na(aad$Cutaneous.Leishmaniasis))
aad <- subset(aad, aad$Cutaneous.Leishmaniasis > 0)
aad <- subset(aad, !is.na(aad$enn_mn_forest))
ggplot(data = aad, mapping = aes(enn_mn_forest, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()
set.seed(22)
sample <- sample_n(aad, 500)
ggplot(data = sample, mapping = aes(enn_mn_forest, Cutaneous.Leishmaniasis, xlab = "Land Surface Temperature", ylab = "Cases of Cutaenous Leishmaniasis per Thousand")) +
geom_point()